1. Technical Field
The present invention relates to a channel knowledge acquisition system and a method thereof, and more particularly to a system for acquiring channel knowledge with low training overhead by exploiting sparse nature of physical channel and a method thereof.
2. Description of Related Arts
Typically, in order to adopt beamforming techniques to compensate for high expected pathloss of signals in high-frequency bands, the channel knowledge is necessary at both a transmitter (or referred to as a base station) and a receiver (or referred to as an user equipment), wherein the channel knowledge usually includes angles-of-departure and angles-of-arrival of dominant propagation paths. In the conventional all-digital beamforming architecture, channel knowledge can be estimated based on utilization of reference signals in baseband. However, the acquisition of channel knowledge suffers from a subspace sampling limitation in the hybrid beamforming structure, where antenna array is driven by only a limited number of radio frequency chains (RF-chains) to avoid the fabrication cost and energy consumption from massive number of high-frequency mixed-signal components. In other words, the baseband cannot directly observe the entire channel dimension in the hybrid beamforming structure. To address this challenge, the training-based approach referred to as beam training is generally adopted to perform channel subspace sampling on an unknown channel by sweeping a plurality of transmission beams at the base station side and a plurality of reception beams at the user equipment side, respectively. After both sides complete the beam-sweeping, the user equipment may determine the channel knowledge according to the measurement result and the beam-related information associated with these beams.
In a straightforward approach for beam training, a set of directional transmission beams and reception beams with required resolutions are employed at both a base station and an user equipment to exhaustively scan all possible directions of physical channel, respectively. After the beam-sweeping, each user equipment finally reports indexes corresponding to beam pairs with high received power or good channel quality to the base station as channel knowledge. However, the exhaustive search method needs to sweep an extremely high number of directional beams especially for beamforming systems with massive antennas. Hence, it is difficult to obtain sufficient channel subspace samples within a practical coherence time to compute a meaningful estimate. Moreover, the training overhead is significant.
In view of this, some vendors propose technical solutions of hierarchical search method, which performs beam training by utilizing multi-resolution beam-codebooks collocated with divide-and-conquer search. A coarse-resolution beam-codebook may contain a small number of wide directional beams covering an intended spatial area, while a fine-resolution codebook may contain a large number of narrow directional beams covering the same intended spatial area, and that a wide directional beam may have the same/similar coverage as that of multiple narrow directional beams together. The divide-and-conquer search based on the feedback information is then carried out across the hierarchy of these codebooks, by determining the wide directional beam with the best received power or channel quality first on the coarse-resolution beam-codebook level, and then the narrow directional beam with the best received power or channel quality on the fine-resolution beam-codebook level. Both the base station and user equipment proceed in refining the angles-of-departure/angles-of-arrival associated with the dominant propagation paths until reach the required resolution. The required resolution is typically directly proportional to the number of antennas in beamforming systems. Compared with the exhaustive search method, the required training overhead in hierarchical search method increases with the number of antennas in log manner rather than linear manner. Therefore, the training overhead can be significantly reduced compared to the exhaustive search method.
However, the hierarchical search method has several main disadvantages. For example, hierarchical training procedure requires the usage of feedback channel during the training procedure. It may be challenging due to the complicated control plane and the unreliable transmission/reception without sufficient beamforming gains before beam training is completed. Moreover, when multiple user equipments are served in the same cell, the base station needs to search each possible direction according to the feedback information reported by each user equipment. Thus, the training overhead of the hierarchical search method may grow linearly with the increasing number of user equipment. It will be a major concern when we determine its adoption in multi-user cellular systems.
In summary, it can be seen that the prior art has encountered several issues of high training overhead for a long time. Therefore, it is necessary to propose an improved technical solution to resolve these problems.